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Bayesian methods use probabilities to model uncertainty in data from "summary" of Machine Learning by Stephen Marsland
Bayesian methods are a powerful tool in machine learning that leverage probabilities to capture uncertainty inherent in data. By using probabilities, we are able to represent our belief in the various possible outcomes of a given situation. This is particularly useful in situations where the data is noisy or incomplete, as it allows us to make informed decisions even in the presence of uncertainty. In Bayesian methods, we start by defining a prior distribution that represents our initial beliefs about the parameters of interest in our model. As we observe data, we update this prior distribution using Bayes' theorem to obtain a posterior distribution that reflects our updated beliefs. This iterative process of updating our beliefs as we observe more data is at the core of Bayesian inference. One of the key advantages of Bayesian methods is their ability to incorporate prior knowledge into the model. This is especially useful in situations where we have relevant information that can help us better estimate the parameters of interest. By incorporating prior knowledge, we are able to make more informed predictions and improve the overall performance of our model. Furthermore, Bayesian methods provide a principled way to perform model selection and regularization. By placing priors over model parameters, we are able to control the complexity of the model and prevent overfitting. This helps us avoid making overly complex models that might perform well on the training data but generalize poorly to unseen data.- Bayesian methods offer a flexible and robust framework for modeling uncertainty in data. By using probabilities to represent uncertainty, we are able to make more informed decisions, incorporate prior knowledge, perform model selection, and regularization. This makes Bayesian methods a valuable tool in the field of machine learning.
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